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University of Pretoria Creates an @RISK Model for Stopping the Spread of Avian Flu

The avian influenza virus – or avian flu – is a fast-spreading infection that affects poultry and potentially people worldwide. While the risk to humans is not completely understood, stopping human exposure to the virus is critical. According to Dr. Folorunso Oludayo Fasina, a senior lecturer at the University of Pretoria’s Department of Production Animal Studies, it is important to understand “how the virus gets into the food system, how it spreads and how it can be managed. To do this, we need risk assessment and exposure assessment, as well as a response model. Once we have this information, we can implement measures to stop the risks.”

Dr. Fasina and his colleagues created a model for foodborne contamination that was specific to Africa, where the virus has already infected 12 countries. The team studied both biological and cultural aspects, including food processing, trade, and cooking-related practices, and collected data from more than 375 Egyptian and Nigerian sites including homes, local producers, live bird markets, village and commercial abattoirs and veterinary agencies. As a first step, the team used Palisade’s TopRank tool, part of the DecisionTools Suite, to analyze the sensitivity of each of the identified contributors to the overall risk. This helped the team understand which of the contributors were the most important.

Next, the team moved to @RISK to help predict the different ways the virus could be spread. Using Monte Carlo simulation, @RISK can quantify the probabilities of different outcomes – or infection rates – occurring, as well as determine the optimal preventive measures to mitigate the risk of animal-to-person infection.

The results revealed numerous opportunities for the avian influenza virus to be spread, and found that the estimated risk for humans was higher than previously reported. Says Dr. Fasina, “@RISK is a valuable tool to investigate these problems and do risk predictions either prospectively or retrospectively. Utilizing the outputs from models like this can help health policy planners and public health officials to take anticipatory measures to prevent future disasters associated with infectious diseases like the avian flu.”